HMPBE 2013 - Special Session on Homotopy Methods for Progressive Bayesian Estimation
Topics/Call fo Papers
This session is concerned with homotopy methods for the efficient solution of Bayesian state estimation problems occurring in information fusion and filtering. For state estimation in the presence of stochastic uncertainties, the best current estimate is represented by a probability density function. For that purpose, different representations are used including continuous densities such as Gaussian mixtures or discrete densities on continuous domain such as particle sets. Given prior knowledge in form of such a density, the goal is to include new information by means of Bayes' theorem. Typically, the resulting posterior density is of higher complexity and difficult to compute. In the case of particle sets, additional problems such as particle degeneracy occur. Hence, an appropriate approximate posterior has to be found. For recursive applications, this approximate posterior should be of the same form as the given prior density (approximate closedness). To cope with this challenging approximation problem, a well-established technique is to gradually include the new information instead of using it in one shot, which is achieved by a homotopy. For this session, manuscripts are invited that cover any aspect of homotopy methods for state estimation. This includes both theoretically oriented work and applications of known methods.
Other CFPs
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- Special Session on Intelligent Information Fusion
- 1st International Workshop on Applications of Affective Computing in Intelligent Environments
- 14th Towards Autonomous Robotic Systems
Last modified: 2013-02-26 22:06:29